偏差在神经网络中的作用是什么？ [关闭]

我知道梯度下降和反向传播算法。我不明白的是：什么时候使用偏差很重要以及如何使用它？

例如，当映射AND函数，当我使用两个输入和一个输出时，它没有给出正确的权重。但是，当我使用三个输入（其中之一是偏差）时，它会给出正确的权重。

我认为偏见几乎总是有帮助的。有效，偏置值允许您将激活函数向左或向右移动，这对于成功学习可能至关重要。

看一个简单的例子可能会有所帮助。考虑这个没有偏差的 1 输入、1 输出网络：

The output of the network is computed by multiplying the input (x) by the weight (w₀) and passing the result through some kind of activation function (e.g. a sigmoid function.)

Here is the function that this network computes, for various values of w₀:

Changing the weight w₀ essentially changes the "steepness" of the sigmoid. That's useful, but what if you wanted the network to output 0 when x is 2? Just changing the steepness of the sigmoid won't really work -- you want to be able to shift the entire curve to the right.

这正是偏见允许你做的事情。如果我们向该网络添加偏差，如下所示：

...then the output of the network becomes sig(w₀*x + w₁*1.0). Here is what the output of the network looks like for various values of w₁:

Having a weight of -5 for w₁ shifts the curve to the right, which allows us to have a network that outputs 0 when x is 2.